class_docstr: Class for computing metrics based on two StructuredDatasets.
docstr: Compute the average Euclidean distance between the samples from the
  two datasets.
docstr_tok: ["Compute","the","average","Euclidean","distance","between","the","samples","from","the","two","datasets","."]
code: def euclidean_distance(self, privileged=None, returned=False):
      """Compute the average Euclidean distance between the samples from the
      two datasets.
      """
      condition = self._to_condition(privileged)
      distance, mask = utils.compute_distance(
          self.dataset.features,
          self.distorted_dataset.features,
          self.dataset.protected_attributes,
          self.dataset.protected_attribute_names,
          dist_fun=scdist.euclidean,
          condition=condition,
      )
      if returned:
          return distance, self.dataset.instance_weights[mask]
      return distance
  
imports: from functools import partial
  import numpy
  import scipy.spatial.distance
  from aif360.datasets import StructuredDataset
  from aif360.metrics import DatasetMetric, utils
label_desc: Use image node_id 5 for calling the SampleDistortionMetric obj's underlying member method code with example usage: obj.euclidean_distance(privileged, returned) and returns: distance, distance,  self